Tuesday, October 29

OpenTox - Development of Open Standards, Open Source and Open Data supporting Predictive Toxicology, Barry Hardy (Douglas Connect)

One important goal of OpenTox is to support the development of an Open
Standards-based predictive toxicology framework that provides a unified
access to toxicological data and models. OpenTox supports the
development of tools for the integration of data, for the generation and
validation of in silico models for toxic effects, libraries for the
development and integration of modelling algorithms, and scientifically
sound validation and reporting routines.

The OpenTox Application Programming Interface (API) is an important open
standards development for software development purposes. It provides a
specification against which development of global interoperable
toxicology resources by the broader community can be carried out. The
use of OpenTox API-compliant web services to communicate instructions
between linked resources with URI addresses supports the use of a wide
variety of commands to carry out operations such as data integration,
algorithm use, model building and validation. The OpenTox Framework
currently includes, with its APIs, services for compounds, datasets,
features, algorithms, models, ontologies, tasks, validation, reporting,
investigations, studies, assays, and authentication and authorisation,
which may be combined into multiple applications satisfying a variety of
different user needs. As OpenTox creates a semantic web for toxicology,
it should be an ideal framework for incorporating toxicology data,
ontology and modelling developments, thus supporting both a mechanistic
framework for toxicology and best practices in statistical analysis and
computational modelling.

The NTP DrugMatrix Database, Scott Auerbach (NIEHS)

DrugMatrix is the scientific communities' largest molecular toxicology
reference database and informatics system. DrugMatrix is populated with
the comprehensive results of thousands of highly controlled and
standardized toxicological experiments in which rats or primary rat
hepatocytes were systematically treated with therapeutic, industrial,
and environmental chemicals at both non-toxic and toxic doses. Following
administration of these compounds in vivo, comprehensive studies of the
effects of these compounds were carried out at multiple time points and
in multiple target organs. These studies included extensive
pharmacology, clinical chemistry, hematology, histology, body and organ
weights, and clinical observations. Additionally, a curation team
extracted all relevant information on the compounds from the literature,
the Physicians' Desk Reference, package inserts, and other relevant
sources. The heart of the DrugMatrix database is large-scale gene
expression data generated by extracting RNA from the toxicologically
relevant organs and tissues and applying these RNAs to the GE Codelink™
10,000 gene rat array and more recently the Affymetrix whole genome 230
2.0 rat GeneChip® array. DrugMatrix contains toxicogenomic profiles for
638 different compounds; these compounds include FDA approved drugs,
drugs approved in Europe and Japan, withdrawn drugs, drugs in
preclinical and clinical studies, biochemical standards, and industrial
and environmental toxicants. Contained in the database are 148 scorable
genomic signatures derived using MOSEK computational software that cover
96 distinct phenotypes. The signatures are informative of
organ-specific pathology (e.g., hepatic steatosis) and mode of
toxicological action (e.g., PXR activation in the liver). The phenotypes
cover a number of common target tissues in toxicity testing (including
liver, kidney, heart, bone marrow, spleen and skeletal muscle). The
primary value that DrugMatrix provides to the toxicology community is in
its capacity to use toxicogenomic data to perform rapid toxicological
evaluations. Further value is provided by DrugMatrix ontologies that
help characterize mechanisms of pharmacological/toxicological action and
identify potential human toxicities. Overall, DrugMatrix allows a
toxicologist to formulate a comprehensive picture of toxicity with
greater efficiency than traditional methods.

The ToxCast assay library currently includes 328 high-throughput
screening (HTS) assays with a total of 555 readouts or components.
Assessing a battery of cellular, pathway, protein and gene inhibitions
and stimulations, the total number of assay endpoints
(assay_component_endpoints) generated was 826. Selection of assay
endpoints encounters questions such as suitable cell type, assay design
specifics, protein form, and detection method. To simplify these
concepts, the BioAssay Ontology was applied to annotate 36 descriptors
for assay design and target information and to create a scheme for
identifying assay_component_endpoints. Assay design information is
distinguished by assay design types (n= 22), detection technology types
(n=11), and cell types (n=36), which consists of 14 species and 18
tissues. ToxCast assay target information is separated by technological
target mapped to gene(s) (n=381), technological target types (n=15),
intended target genes (n= 362), intended target families (n=23),
intended target types (n=14), and biological process targets (n=11).
Differentiating technological and intended target information created a
distinction between the measured target and the purpose or intention of
running the assay. For example, a cell proliferation assay was run in
T47D cells in which the primary readout and target was cell
proliferation. However, the intention of the assay was to detect
estrogen receptor agonists as T47D cells are estrogen responsive. For
purposes of quality control and data aggregation, we are now able to
rapidly group this particular assay with other estrogen receptor
targeting assays. In conjunction with assay annotation, we have also
developed a data analysis pipeline that encompasses the many stages of
HTS data processing, including raw data management, chemical and assay
mapping, data normalization, outlier detection, concentration response
modeling and hit-calling. We have created an 8 level system for
analyzing the millions of concentration response curves generated as
part of the ToxCast research program. In conjunction, the assay
annotation and data analysis pipeline foster data transparency and
sharing improving downstream modeling and increasing the odds of
incorporation of HTS data into the chemical safety decision process.

The CEBS Database, a Structure for Data Integration across Studies, Asif Rashid (NIEHS)

The Chemical Effects in Biological Systems (CEBS) Database at the US
National Toxicology Program (NTP) is designed to archive legacy NTP data
and to support integration across studies in CEBS. CEBS also houses
public studies from industry, academic labs, and from other government
labs, making it a resource for the environmental health sciences. CEBS
houses primary data from individual animals and from in vitro exposures
of cultured cells. This presentation will give three examples of
accessing CEBS by means of the user interface, discuss meta data in
CEBS and the CEBS data dictionary, and briefly discuss a project to make
CEBS legacy data available using an RDF triple store.

Implementation of Systematic Review, Kristina Thayer (NTP)

The NTP Office of Health Assessment and Translation (OHAT) conducts
literature-based evaluations to assess the evidence that environmental
chemicals, physical substances, or mixtures (collectively referred to as
"substances") cause adverse health effects and provides opinions on
whether these substances may be of concern given what is known about
current human exposure levels. OHAT also organizes state-of-the-science
evaluations to address issues of importance in environmental health
sciences. In 2012, OHAT began implementing systematic review methodology
to carry out these evaluations to enhance transparency for reaching and
communicating evidence assessment conclusions. The systematic review
format provides increased transparency through a detailed protocol that
outlines each step in an evaluation including procedures used to perform
a literature search, determine whether studies are relevant for
inclusion, extract data from studies, assess study quality, and
synthesize data for reaching conclusions. The method for data synthesis
includes steps to assess confidence within an evidence stream (i.e.,
human, animal, and other relevant data separately) and then to integrate
across evidence streams to reach hazard identification conclusions.
This presentation will discuss some of the basic concepts of systematic
review and highlight the software-based information management tools
utilized by OHAT in order to implement systematic review, visually
display data, and manage data warehousing for manually curated human,
animal, and in vitro studies in a format that facilitates data sharing
and mining across agencies and the public.

The COSMOS consortium, a part of the SEURAT-1 cluster, has been engaged in
development of computational approaches to enable alternatives to animal
testing. The COSMOS database provides the management and sharing of
chemical, biological and toxicity data to support activities. This
database uses a compound-centered data model that can store both
chemical and toxicological data. The chemistry content includes a
cosmetics inventory compiled from EU COSING and US PCPC for cosmetics
ingredients and related chemicals. The database supports dose-level
effects data from repeated dose toxicity studies, metabolism information
and dermal penetration/absorption data. A user-friendly web-based user
interface provides a searching/retrieval system as well as links to
other SEURAT resources. The data have been collected, curated,
quality-controlled, stored and managed in a flexible and sustainable
manner to support predictive modeling tasks. Many such modeling tasks
begin by connecting biological effects and chemicals involved in
pathways. The in vivo oral repeated dose toxicity database
(oRepeatToxDB) is part of the COSMOS DB. The controlled vocabulary for
oRepeatToxDB allows phenotypic effects to be linked to cellular events.
Toxicity effects observed at target organ sites have been organized
hierarchically to relate organs to tissues to cells, while also mapping
biological processes to phenotypic effects. Thus, oRepeatToxDB is well
suited for data mining to elucidate site/effect combinations and to be
used as a basis for building toxicity ontology for relating chemistry
mechanistically to effects in toxicity pathways. A case study
investigating the relationship between liver steatosis and fibrosis and
the underlying morphological changes caused by, for example, analogs of
retinoids as well as aromatic amines will be discussed from a mechanistic
perspective. This methodology provides a systematic approach for
investigating chemically-induced toxicity and elucidating the underlying
mechanism, and may further guide studies to determine the mode of
action for hepatotoxicity.

A mechanistic toxicology has evolved over the last decades, which is
effectively relying to large extent on methodologies which substitute or
complement traditional animal tests. The accompanying biotechnology and informatics
revolution has made such technologies broadly
available and useful. Regulatory toxicology has only slowly begun to
embrace these new approaches. Major validation efforts, however, have
delivered the evidence that new approaches do not necessarily lower
safety standards and can be integrated into regulatory safety
assessments, especially in integrated testing strategies. Political
pressures especially in the EU, such as the REACH legislation and the
7th amendment to the cosmetic legislation, further prompt the need of
new approaches. In the US, especially the NAS vision report for a
toxicology in the 21st century and its most recent adaptation by the US EPA for
their toxicity testing strategy have initiated a debate on how to create a
novel approach based on human cell cultures, lower species,
high-throughput testing and modeling. The report suggests moving away
from traditional (animal) testing to modern technologies based on
pathways of toxicity. These pathways of toxicity could be modeled in
relatively simple cell tests, which can be run by robots. The goal is to
develop a public database for such pathways, the Human Toxome, to
enable scientific collaboration and exchange.
The problem is that the respective science is only emerging. What
will be needed is the Human Toxome as the comprehensive pathway list, an
annotation of cell types, species, toxicant classes and hazards to
these pathways, an integration of information in systems toxicology
approaches, the in-vitro-in-vivo-extrapolation by reversed dosimetry and
finally making sense of the data, most probably in a probabilistic way.
The NIH has been funding since September 2011 by a transformative research
grant our Human Toxome project (humantoxome.com). The project
involves US EPA ToxCast, the Hamner Institute, Agilent and several
members of the Tox-21c panel. The new approach is shaped around
pro-estrogenic endocrine disruption as a test case.
Early on, the need for quality assurance for the new approaches as a
sparring partner for their development and implementation has been
noted. The Evidence-based Toxicology Collaboration (EBTC, www.ebtox.com) was created in the US and Europe in 2011 and 2012,
respectively. This collaboration of representatives from all
stakeholder groups aims to develop tools of Evidence-based Medicine for
toxicology, with the secretariat run by CAAT. All together, Tox-21c and
its implementation activities including the Human Toxome and the EBTC
promise a credible approach to revamp regulatory toxicology.

The SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1)
research cluster is comprised of seven EU FP7 Health projects and is
co-financed by Cosmetics Europe. The aim is to generate a
proof-of-concept to show how the latest in vitro and in silico
technologies, systems toxicology and toxicogenomics can be combined to
deliver a test replacement for repeated dose systemic toxicity testing
on animals. The SEURAT-1 strategy is to adopt a mode-of-action framework
to describe repeated dose toxicity to derive predictions of in vivo
toxicity responses. ToxBank is the cross-cluster infrastructure project
whose activities include the development of a data warehouse to provide a
web-accessible shared repository of research data and protocols.
Experiments are generating dose response data over multiple timepoints
using different omics platforms including transcriptomics, proteomics,
metabolomics, and epigenetics over a variety of different cell lines and a
common set of reference compounds. Experimental data is also being
generated from functional assays and bioreactors, and supplemented with
in silico approaches including kinetic information. This complex and
heterogeneous data is being consolidated and harmonized through the
ToxBank data warehouse. It is being organized in order to perform an
integrated data analysis and ultimately predict repeated dose systemic
toxicity. Core technologies used include the ISA-Tab universal data
exchange format, Representational State Transfer (REST) web services,
the W3C Resource Description Framework (RDF) and the OpenTox standards.
We describe the design of the data warehouse based on cluster
requirements, the implementation based on open standards, and illustrate
using a data analysis case study.

Creation of microfluidic bioreactor to assess in vitro long-term fibrogenic toxicity of drugs and cosmetics on liver, Pau Sancho-Bru (Institut d’Investigacions Biomèdiques August Pi i Suyner)

The Cosmetics Europe industry together with the European Commission launched
a Research Initiative focused on the "Safety Evaluation Ultimately
Replacing Animal Testing (SEURAT)". SEURAT-1 aims at trying to fill
current gaps in scientific knowledge and accelerate the development of
non-animal test methods. In this respect, HeMiBio is one of the six
research projects funded under the SEURAT-1 cluster umbrella, with the
specific aim of developing a device that simulates the complex structure
of the human liver for preclinical toxicity testing. One of the
frequent adverse outcomes in toxicity testing under chronic exposure is
liver fibrosis. Liver fibrosis results as a respose to a persistent
liver injury. In most cases, hepatocytes are the primary target cells
due to their high metabolic activity, but Hepatic Stellate Cells (HSC),
Liver Sinusoidal Endothelial Cells (LSEC) or macrophages may also be
directly targeted. Irrespectively of the molecular initiating event, HSC
activation and the acquisition of a myofibroblastic state is the key
event leading to extracellular matrix deposition and liver fibrosis.
In order to evaluate this process in vitro in a repeated dose
toxicity testing we need to develop new tools able to mimic the complex
interaction among liver cells that occur during a fibrogenic stimulus.
In vitro fibrosis assessment will require the development of: 1-
expansion of existing current knowledge on the different liver cell
types, particularly HSCs and LSEC; 2- a reproducible human Hepatocyte,
HSC and LSEC cell source; 3- co-culture systems to maintain cells in an
optimal phenotype; and 4- a device able to maintain liver cells in a
co-culture setting and maintaining their interaction. This presentation
will introduce the efforts taken by HeMiBio to accomplish these four
goals and will focus specifically on the work performed to
characterize primary HSCs and to develop methods for obtaining HSCs from
induced pluripotent stem cells for toxicity testing.

The data from the US EPA ToxCast program has been the subject of active
discussion in the literature recently. In the talk, I will provide a
balanced view on the quality and predictability of the data from a
statistical point of view and present the results of large-scale
computational experiments along those lines. I will show that, using
so-called multi-label classification, dependencies among toxic effects
in the ToxCast data set can be exploited successfully. A filtering step
by an internal leave-one-out cross-validation filters those endpoints
that can be predicted worse than by random guessing and additionally
those that do not benefit from a joint prediction together with other
endpoints by multi-label classification. As a result of our experiments,
we obtain a list of in vivo endpoints that can be predicted with some
confidence and a set of related endpoints that benefit from a concerted
prediction.

A consensus partial least squares
(PLS)-similarity based k nearest neighbors (KNN) model was developed utilizing
3D-SDAR (three dimensional spectral data-activity relationship) fingerprint
descriptors for prediction of the log(1/EC50) values of a dataset of 94 aryl
hydrocarbon receptor (ArH) binders. This consensus model was constructed from a
PLS model utilizing 10 ppm x 10 ppm x 0.5 Å bins and 7 latent variables (R2test
of 0.617), and a KNN model using 2 ppm x 2 ppm x 0.5 Å bins and 6 neighbors (R2test
of 0.622). Compared to individual models, improvement in predictive performance
of approximately 10.5% (R2test of 0.685) was observed. Further
experiments indicated that this improvement is likely an outcome of the
complementarity of the information contained in 3D-SDAR matrices of different bin
granularity. For similarly sized data
sets of AhR binders the consensus KNN and PLS models compare favorably to
earlier reports. The ability of PLS
QSDAR models to provide structural interpretation was illustrated by a
projection of the most frequently occurring bins on the standard coordinate
space, thus allowing identification of structural features related to
toxicity. Consensus QSDAR modeling
results for other toxicological endpoints will also be provided.

The views presented in this article are
those of the authors and do not necessarily reflect those of the US Food and
Drug Administration. No official endorsement
is intended nor should be inferred.

Wednesday, October 30

In Vitro and In Vivo Extrapolation in Predictive Toxicology, Harvey Clewell (Hamner Institute)

The field of toxicology is currently undergoing a global paradigm shift
to use of in vitro approaches for assessing the risks of chemicals and
drugs, yielding results more rapidly and more mechanistically-based than
current approaches relying primarily on in vivo testing. However,
reliance on in vitro data entails a number of new challenges associated
with translating the in vitro results to corresponding in vivo
exposures. Physiologically-based pharmacokinetic (PBPK) models provide
an effective framework for conducting quantitative in vitro to in vivo
extrapolation (IVIVE). Their physiological structure facilitates the
incorporation of in silico- and in vitro-derived chemical-specific
parameters in order to predict in vivo absorption, distribution,
metabolism and excretion. In particular, the combination of in silico-
and in vitro parameter estimation with PBPK modeling can be used to
predict the in vivo exposure conditions that would produce chemical
concentrations in the target tissue equivalent to the concentrations at
which effects were observed with in vitro assays of tissue/organ
toxicity. This presentation will describe the various elements of IVIVE
and highlight key aspects of the process including: (1)
characterization of free concentration, metabolism, and cellular uptake
in the toxicity assay; (2) conversion of in vitro data to equivalent
PBPK model inputs, and (3) potential complications associated with
metabolite toxicity. Two examples of PBPK-based IVIVE will be
described: a simple approach using whole hepatocyte clearance and plasma
binding that is suitable for a high-throughput environment, and a more
complicated approach for a case of metabolite toxicity.

Prioritization of thousands of environmental chemicals requires reliable
methods for screening on both hazard and exposure potential.
High-throughput biological activity assays allow the ToxCast™ and Tox21
projects to compare the in vitro activity of chemicals with known in
vivo toxicity to those with little or no in vivo data. Additional in
vitro assays characterize key aspects of pharmacokinetics and allow in
vitro-in vivo extrapolation to predict human uptake (mg/kg BW/day) that
might be sufficient to cause the observed in vitro bioactivity in vivo,
thereby identifying potential hazard. Without similar capability to make
quantitative, albeit uncertain, forecasts of exposure, the putative
risk due to an arbitrary chemical cannot be rapidly evaluated. Using
physico-chemical properties and provisional chemical use categories,
most of the ~8,000 Tox21 chemicals have been evaluated with respect to
exposure from near field sources, i.e. proximate exposures in the home
that might result from, for example, the use of consumer products. A
Bayesian methodology was used to infer ranges of exposures consistent
with biomarkers measured in urine samples by the National Health and
Nutrition Examination Survey (NHANES). For various demographic groups
within NHANES, such as children aged 6-11 and 12-18, males, and females,
we consider permutations of linear regression models, including as few
as one and as many as seventeen available and seemingly relevant
physico-chemical and use factors in order to select the most
parsimonious model. Each demographic-specific linear regression provides
a predictor, calibrated to the NHANES data, which can then be applied
to the remainder of the Tox21 list. The variance of this calibration
serves as an empirical determination of uncertainty. These exposure
predictions are then directly compared to the doses predicted to cause
bioactivity for 231 ToxCast chemicals for which in vitro-in vivo
extrapolation to mg/kg BW/day doses has been performed. For chemicals
without any exposure data, the models determined by this method can
predict a likely range for the average human exposure due to near field
sources. When combined with high throughput hazard data, these exposures
can allow chemical research prioritization on the basis of probable
risk. This abstract does not necessarily reflect U.S. EPA policy.

HAWC (https://hawcproject.org/) is a modular, cloud-ready,
informatics-based system to synthesize multiple data sources into
overall human health assessments of chemicals. Developed in
collaboration with EPA/NCEA, this system seamlessly integrates and
documents the overall workflow from literature search and review, data
extraction and evidence synthesis, dose-response analysis and
uncertainty characterization, to creation of customized reports. Crucial
benefits of such a system include improved integrity of the data and
analysis results, greater transparency, standardization of data
presentation, and increased consistency. By including both a web-based
workspace for assessment teams who can collaborate on the same
assessment rather than share files and edits, and a complementary
web-based portal for reviewers and stakeholders, all interested parties
have dynamic access to completed and ongoing assessments.

Multi-scale data integration and modeling, Tom Knudsen (US EPA)

Multiscale modeling and simulation is an important approach for
discovery and synthesis of biological design principles underlying the
response of complex adaptive systems to perturbation. Virtual Tissue
Models (VTMs) integrate empirical data with information that can be
mapped to dynamic biological tissue architectures relevant to Adverse
Outcome Pathway (AOP) elucidation. VTMs bring together in vitro data
from diverse assay and high-throughput screening (HCS) platforms with
biological information on dynamic cellular behaviors in a computational
systems biology context. Such models might be useful to predict the
potential impact of chemical perturbations on higher-order biological
organization and functions. In silico VTMs engineered with CompuCell3D
(www.compucell3d.org) and other resources are being used to simulate
biological design principles underlying important developmental
processes such as blood vessel development, somite formation, palatal
fusion, male urethral morphogenesis, and limb-bud outgrowth. The
simulations engage the normal biology of a complex adaptive system based
on cellular-molecular knowledge and understanding of embryogenesis.
Perturbing embryological VTMs with HTS and in vitro data has the
potential to assess the plausibility of different tissue-level changes
in leading to AOPs relevant to human developmental toxicity.
Embryological VTMs also enable different modeled exposures to be
evaluated for early lifestage considerations. This abstract does not
necessarily reflect US EPA policy.

From bench to FDA, validation of in vitro methods: who is responsible for independent validation of human biology-based tests? Katya Tsaioun (Safer Medicines Trust)

Every organization be it industry, academic or government organizations involved in safety testing of new chemical entities agrees that the current system of toxicity testing is not as predictive of human outcomes as we need. Though the current paradigm has been "tried" for decades, it is hard to call it "true" to human outcomes, when it misses 94% of human toxicities. So what are we as industry, government, charities and foundations doing about it? We will review the roles of all stakeholders in validation and adoption of alternative methods: industry, regulatory authorities, alternative technology inventors (academic and industry) and independent, government and non-profit organizations. Paths to validation and acceptance of alternative methods in different industries will be discussed. The role of government initiatives will be discussed using examples of the cosmetics industry 2013 ban on use of animal testing and the REACH directive. A new paradigm for faster validation and industry adoption of alternative methods will be presented with a consortium of independent organizations managing the process. Safer Medicines Trust is a non-profit organization whose mission is to improve patient safety via including human biology-based methods into the drug regulatory approval process. An outline of the independent pilot validation study proposed by Safer Medicines Trust will be presented for discussion.

A regulatory science priority at the US Food and Drug Administration
(FDA) is to promote the development of new innovative predictive tools
to support safety evaluations of regulated products. One predictive
method in current use as investigative and applied safety science tools
are well validated and defined computational (in silico) quantitative
structure-activity relationship (QSAR) models. In this FDA Critical Path
Initiative project, predictive in silico QSAR models were developed
based on clinical trials of drugs reviewed at CDER. These models are
being tested as clinical decision-support tools for CDER evaluations of
the proarrhythmic potential of non-antiarrhythmic drugs. Assessing
drug-induced proarrhythmia is of regulatory interest to public health
since it may present as sudden cardiac death in patients.
Classification models were built using two different QSAR predictive
technologies. Drugs from clinical QT prolongation assessments and
thorough QT (TQT) studies comprised the training sets of the models and
these data focus on the regulatory threshold of concern for QT/QTc
prolongation using exposure-response testing in humans and risk-based
evidence of torsade de pointes (TdP) per ICH E14 guidance. This
presentation will cover the development of these unique QSAR models and
the implications for use in risk prioritization in a CDER regulatory
safety and risk assessment setting of the evaluation of drug-induced
proarrhythmia in humans. Based on external validation, the models
perform exceptionally well, and take into account exposure-response
relationships, metabolic activation, and assay sensitivity as well as
other standards that establish a regulatory acceptable clinical trial.
Predictive performance for both in silico technologies was observed to
have high and desirable values, and was judged based on external
validation whenever possible. The Leadscope clinical QT/QTc
prolongation model which was enriched with the mechanistic knowledge of
potent hERG blockers, demonstrated concordance of 82%, sensitivity 86%,
specificity 78%, a positive predictive value (PPV) 82%, and negative
predictive value (NPV) of 82%. Y-scrambling results provided supporting
evidence of the predictive power of the final model over random. A
QSAR model was built with the Prous Institute’s Symmetry℠ technology to
forecast TdP activity using a training set of high risk drugs withdrawn
from the market due to QT prolongation and TdP concerns, and which
induce exceptionally high QT/QTc interval effects > 20 msec.
Validation of this model demonstrated exceptional concordance of 91%,
sensitivity 87%, specificity 92%, PPV 85%, and NPV 93%. Collectively,
these in silico models represent innovative clinical decision-support
tools to detect risk through computational assessment of drug molecular
structure for forecasting the proarrhythmic potential of new drugs in a
regulatory risk assessment setting.

The (U.S.) National Library of Medicine (NLM) is the world’s largest
biomedical library, with the mission of collecting, organizing,
preserving, and disseminating health-related information. NLM's
resources are available for free by global users, with the Specialized
Information Services Division (SIS, sis.nlm.nih.gov) responsible
for information resources and services in toxicology, environmental
health, chemistry, and other topics. This includes databases and special
topics Web pages, with “what you need, when you need it, where you need
it, and how you need it” access to many of its resources as downloads,
Smartphone apps, and/or as Web pages optimized for mobile devices. One
example of an NLM SIS resource is NLM’s TOXNET “one stop access” set of
numerous databases (TOXicology Data NETwork, toxnet.nlm.nih.gov). For example, TOXNET’s TOXLINE provides
bibliographic information covering the toxicological and other effects
of chemicals, and incorporates content from NLM’s PubMed/MEDLINE. Also,
TOXNET’s Hazardous Substances Data Bank (HSDB®) continues to be enhanced
to include new materials. HSDB’s first set of nanomaterials was added
in late 2009, and NLM SIS has since included additional nanomaterials
and other substances of emerging interest to toxicologists and others.
NLM SIS is also seeking to add state-of-the-science toxicology,
exposure, and risk assessment information, and images (e.g., metabolic
pathways) to HSDB. Further, NLM SIS recently developed an enhanced
version of its ALTBIB Web portal to provide better access to information
on in silico, in vitro, and improved (refined) animal testing methods,
along with information on the testing strategies incorporating these
methods and other approaches. Other efforts include providing improved
access to genomics-related information, one example being the addition
in 2011 of the Comparative Toxicogenomics Database (CTD) to TOXNET.
Another area of interest is to help provide access to information from
Tox21, ToxCast, ExpoCast, and other similar efforts around the world. A
further set of NLM SIS–developed resources is the Enviro-Health Links
collection of online pages (EHLs, sis.nlm.nih.gov/pathway.html).
The EHLs are Web bibliographies of links to authoritative and
trustworthy online resources in toxicology, environmental health, and
everyday types of exposures such as indoor air pollution. The resources
noted in the EHLs are selected from government agencies and
non-governmental organizations. Many EHLs include sets of
pre-formulated searches of all relevant SIS and NLM databases, allowing
users to search for the most recent citations on a topic of interest.
The “Toxicology Web Links” EHL includes an extensive collection of
“.gov,” “.edu,” “.org,” and “.com” resources judged to meet the
selection criteria for inclusion in an EHL. The resources noted in the
Toxicology Web Links EHL are divided into “U.S. government,”
“international,” “associations and societies,” and “additional
resources” categories, and users of this EHL will note a strong emphasis
on resources providing free online access to information.

Health Canada Contaminated Sites group provides expert support to other federal departments in the area of human health risk assessment and contaminated sites remediation projects. The contaminants can include “Data Rich” and “Data Poor” chemicals. Data poor chemicals often have limited or incomplete experimental toxicology information for extrapolation to human health. At times, data poor chemicals may have undergone extensive toxicity testing; however diverse datasets may make it difficult to extrapolate experimental toxicology information to make it biologically relevant to human health (e.g., perfluorinated chemicals). Persistence in the environment, fate and transport, and transformation and chemical interaction issues potentially have implications from a site remediation perspective. Chlorinated Solvent clusters (Perchloroethylene, Trichloroethylene, Vinyl Chloride, etc.), Petroleum Hydrocarbons and metals can be considered as “Data Rich” clusters. Of interest to the Health Canada contaminated sites group, to inform site remediation and risk management of contaminated sites, is the possible use of predictive toxicology tools in both Data Poor and Data Rich situations based on various considerations of availability of site specific data, location of those sites and availability of resources.

The Health Canada contaminated sites group have developed a Remediation Technology Exposure Check List Tool. The tool uses a selection of technologies with flowcharts and a decision matrix for evaluating human health exposure concerns related to different remediation technologies. A decision analytic process to consider, screen and prioritize chemical contaminants to achieve remediation goals is a complex situation because chemical contaminants can co-occur, interact or competitively bind with each other. For many such situations, insufficient experimental data is directly available to understand their interactions in the context of chemical fate, transport and transformation. Hence, predictive toxicology tools can potentially play an important role in better informing risk managers and site remediation experts so that they could use an extended set of evidences in their decision making and contaminated site management activities.

To address the knowledge gap, predictive tools can provide estimates, if applicable, based on chemical structure (e.g., Structure Activity Relationships (SARs), QSAR modelling, Structural Alerts). Additionally, clustering of chemicals based on available toxicity data and human biology datasets can be used to better inform chemicals that are undergoing remediation as well as residual chemicals that are undergoing changes in the surface and sub-surface environments. When we integrate this evidence in the context of risk management of contaminated sites, we can envisage a potential valuable contribution of predictive modelling based on a combination of risk estimation, toxicokinetics, toxicodynamics, chemical toxicology and interactions (e.g., combined exposure issues from a site remediation perspective) to inform remediation projects so that better health risk management decisions can be taken and so that custodians can effectively utilize their limited resources.

In this study, we selected a small number of remediation sites and identified risk assessment, management and remediation issues faced by the sites in assessing the application of some remediation technologies. We reviewed current practices involved in the design and intended future use of the Remediation Check List Tool. We also reviewed the use of toxicology data resources, databases, and mapped predictive tools in providing value to decision making, including potential use of human health risk characterization data and tools in considering remediation alternatives and a cost-benefit comparison analysis. The research included consideration of contaminant properties, reactivity, fate and transport, and exposure. An integrated data analysis methodology, carried out over multiple sources of in silico, in vitro, and in vivo data, and involving consensus models and a Weight of Evidence framework was investigated. The methodology was applied to the case study and its site contaminants, and used to support risk based predictions and recommendations for management of contaminated sites.

The RISK21 Roadmap is a straightforward, efficient, and systematic way to achieving a transparent assessment of human health risk to chemicals. The Roadmap is a problem-formulation based, exposure-driven, and tiered methodology that seeks to derive only as much data as is necessary to make a safety decision. The Roadmap begins with problem formulation in which a clear statement is made about what information is needed to make a safety decision and criteria that indicate when the problem is solved. Exposure evaluation is brought to the beginning of the assessment rather than at the end, which in turn directly affects the need for toxicological information, including the type of studies to be done and the dose levels to be used. The effectiveness of the RISK21 Roadmap relies on the vast body of exposure and toxicity knowledge that has been built in the last 50 years to provide starting points of reference and precedence. Exposure and toxicity estimates are brought together on a highly visual Matrix that displays the degree of confidence in the estimates and gives guidance as to the most efficient way to generate new data to make a decision on safety. Two case studies were generated by the RISK21 project team. One is a data-rich case study that uses the RISK21 approach in the development of a new pesticide. The second case study focuses on a group of chemicals in drinking water that are data-“poor” and uses the RISK21 approach to prioritize which chemicals are of greatest concern. The RISK21 Matrix itself has the potential to widen the scope of risk communication beyond those with technical expertise, fostering understanding and communication. Being problem formulation and exposure led, it homes in quickly on the scenarios and chemicals that will be of concern. The RISK21 Roadmap and Matrix will reduce the time, resources, and number of animals required to assess a vast number of chemicals. It represents a step forward in the goal to introduce new methodology into risk assessment in the 21st Century.